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PD CEN/TR 17370:2019 Public transport. Operating raw data and statistics exchange

PD CEN/TR 17370:2019

Public transport. Operating raw data and statistics exchange

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Standard number:PD CEN/TR 17370:2019
Pages:134
Released:2019-07-12
ISBN:978 0 539 03469 1
Status:Standard
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PD CEN/TR 17370:2019


This standard PD CEN/TR 17370:2019 Public transport. Operating raw data and statistics exchange is classified in these ICS categories:
  • 35.240.60 IT applications in transport and trade
  • 03.220.99 Other forms of transport

1.1 Introduction

The OpRa work scope is the definition of a minimum set of Public Transport raw data needed as PT quantitative analysis enabling factor. To obtain this considering all the several aspects involved in this complex domain, the work has been conducted through the following phases:

  1. assessment;

  2. use cases definition and classification;

  3. indicators definition;

  4. raw data identification.

OpRa work does not go into the field of service quality measurement and reporting: service quality analysis will of course use data provided by OpRa, but quality definition remains a contractual level issue between a Public Transport Authority and a Public Transport Operator or an operator’s internal choice for a purely private service. OpRa mainly only reports unbiased actual data (i.e. measured or observed), described and aggregated in a shared and understandable way.

The OpRa work documented in detail in this document is coherent with EU Directive 2010/40. In particular, it relates to the Article 42 of the Delegated Regulation EU 2017/1926 [33], as regards the historic data. OpRa proposes to complement NeTEx (dedicated to the static scheduled information), for the historic data based on the underlying conceptual data reference model Transmodel EN 12896, similarly to the requirement of the Delegated Regulation EU 2017/1926 referring to the static scheduled information3.

1.2 Assessment phase

The assessment phase has been conducted studying the following aspects:

  • national scenarios for public transport raw data and statistics exchange, to identify indicators needs and usage;

  • public transport KPI definition in research projects to consider what has been already done in literature and research;

  • relations with public transport EU norms, to be coherent with already existent PT norms.

Moreover, involved actors and stakeholders have been identified like: Public Transport Authority (PTA), Public Transport Agencies, Public Transport Operator (PTO), system integrators and passengers, analysing public transportation Planning and Operation process, that have been divided into five main stages to group all the activates that characterize the Public Transport Service:

  • strategic planning: definition of network elements (lines, stops), main service parameters (vehicles sizes, operation intervals, service intervals for important time demand types), and guaranteed interchanges are planned;

  • tactical planning: operators plan their resource usage (vehicles, rolling stock, personnel), with detailed timetables for each resource unit;

  • before travel: all planned networks and timetables are published. Passengers and other types of clients can plan their use of the offered transportation services via printed and electronic media, and make their reservations as needed;

  • in-travel: the transportation service is conducted. Real-time information exchange is available while this takes place and can be recorded;

  • study and control: in this stage, operators and authorities review the history of actual operations, which could lead to improvements through operational changes, or an optimization of strategic and tactical planning.

The PTA and PTO are interested in all the defined stages, meanwhile from the passenger point of view; only the last three stages are relevant (all the preparation work being hidden).

Figure 3 Public Transport Service phases

image

During the assessment the most relevant research projects results have been considered and a deep analysis of the roles and usage of Public Transport Standards have been completed to guarantee a coherent approach of OpRa.

The first four stages are under the scope of NeTEx (Network Timetable Exchange) and SIRI (Service Interface for Real Time Information) and the last stage is the additional scope to be covered by OpRa. All these standards are compliant with the European Public Transport Reference Data Model (Transmodel).

NeTEx supports data exchange for the Strategic Planning and Tactical Planning stages, with data more than 24 h before validity date. In the Before Travel stage, NeTEx can be used to publish all planned data to client systems. Meanwhile SIRI supports “in-travel / in-operation” data exchange.

The OpRa scope is mainly concentrated to support data exchange for the Study and Control stage and it mainly focus on actual and measured information, i.e. information that cannot be changed anymore in the future. The OpRa covered concepts are based on following Transmodel domains:

  • operations monitoring and control (Part 4);

  • management information and statistics (Part 8).

1.3 Use cases definition and classification phase

To identify the set of raw data, a clear definition of use cases that, based on the assessment phase results, describes the indicators definition and usage to satisfy the Public Transport Study and Control phase is needed.

In this complex and articulate scenario, the work bring to a definition of several use cases this led to the needs to aggregate them and classify accordingly.

1.4 Indicators definition phase

For each defined use case an indicator has been formally defined, including its formulae. It has been advised that some indicators could have particular importance in for the Quantitative Analysis and in this perspective they could be considered Key Process indicators (KPI), for the purpose of this OpRa work, indicators and KPI are used as synonyms.

1.5 Raw data identification phase

After use cases and indicators have been defined, the work enter in the phase of raw data identification for the calculation of the indicators included all use cases. Due to the high level of complexity of the results, a traceability matrix has been identified.